Spectral imaging relates to the acquisition of information about an object, area, or phenomenon by analyzing data acquired by a sensing device not in contact with the object, area, or phenomenon. From this point forward, the term hyperspectral will be used generally to refer to either or both of hyperspectral and multispectral.
Spectral imaging and analysis is used in a wide range of applications. These applications include but are not limited to surveillance, airborne observation of the health and growth of agricultural crops, environmental emissions monitoring, identifying minerals of interest in geological samples, and monitoring and sorting of food products.
Hyperspectral imaging involves acquiring and analyzing information from among several different spectral bands across the electromagnetic spectrum. A hyperspectral image typically consists of a set of images, each image taken in a specific band of the spectrum. FIG. 1 is a representative drawing of an airborne spectral sensor 102 mounted on an aircraft 104 that takes a plurality of images 120, 122, 124, 126, 128 of a single scene, each image being taken in a different spectral band. The set of images taken across different bands together forms a hyperspectral image 110, sometimes called a hyperspectral cube.
In some applications, a hyperspectral image is generated by an airborne sensor. For example, a sensor may be mounted to a satellite or an aircraft, and may take images of the surface of the earth or of the sky. In other applications, a sensor may be positioned in other ways or locations, for example on a tripod directed at a scene on the ground.
One of the major problems in the field of solar reflective (i.e. Visible/Near Infrared/Short Wave Infrared bands or VIS/NIR/SWIR) remote hyperspectral imaging is the influence of the environment, and to some degree of the instrument, on the measurements. Through different physical processes, the environment can modify the apparent spectral signatures of the surfaces being imaged and make them more difficult to identify or in a more general sense to exploit. A role of hyperspectral imagery compensation, also known as correction, is to remove one or more of these effects. The images may then be converted so that the spectra have reflectance units. Reflectance may be a more useful quantity than radiance since it is intrinsic to the observed material and has no dependence on the environment. Reflectance may then be used for target identification or surface characterization.
There are generally two different classes of algorithms that attempt to transform radiance or raw images into reflectance. One class consists of first principles methods that rely on physical knowledge of how the environment interacts with the surfaces to produce radiance. These are sometimes referred to as model based methods or radiative transfer methods. The other class consists of empirical methods, which are sometimes referred to as in-scene methods. Both classes of algorithms have benefits and drawbacks.
First principles methods typically use accurate models of radiative transfer in the atmosphere in order to calculate the environmental effect on the target signature and to remove it. These are fundamental methods that are based on the physical modeling of the environment and its radiative interaction with the measured surfaces. Once this interaction is well understood and can be numerically quantified, it can be removed mathematically from the measured signal to reveal the intrinsic surface characteristics, usually the surface reflectance.
Examples of first principle techniques are Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH™) and Atmospheric and Topographic Correction (ATCOR™-4). Further examples of first principle techniques include Atmospheric CORrection Now (ACORN) and Atmosphere REMoval (ATREM).
First principles methods have a number of advantages, which may include but are not limited to one or more of the following. It may be possible to attain very high precision levels of accuracy in cases where the optical properties of the sensor and environment are known, since the modeling of the phenomenology is well understood and modeled. In addition, the underlying physical understanding of the phenomena that modify the measured signal can lead to the deduction of certain parameters required for the modeling, such as the quantity of water vapor in the air column or the aerosol load. Furthermore, since the correction can be applied on a pixel-by-pixel basis, other local effects can sometimes also be removed. Also, the properties of the surfaces themselves, such as the directionality of their reflectance (e.g. bidirectional reflectance distribution function—BRDF), may also be modeled accurately and contribute to the accuracy or interpretation of the correction. Moreover, prior knowledge of scene elements may not be strictly necessary since atmospheric modeling does not depend on them in a first approximation level, for example if the effect of neighboring surfaces (adjacency) is neglected.
Although first principles modeling may be a very powerful tool for hyperspectral image compensation, there are one or more drawbacks to using them in some circumstances. These disadvantages may include but are not limited to one or more of the following. Accurate radiative transfer calculations can require one or both of a large amount of computer processing power and processing time. In addition, the accuracy of modeling results is sensitive to the characterization of atmospheric parameters such as pressure, temperature and water vapor profiles. In addition, radiative transfer atmospheric compensation techniques generally do not perform well in less than ideal environmental conditions, for example partly to fully overcast conditions and dusty conditions. Furthermore, first principles methods concentrate on the modeling of the atmosphere. Sensor characteristics are usually required to be well known and accounted for before the correction can be applied. If this is not the case large errors are likely to occur.
Again, these are only some possible advantages and disadvantages of first principle (i.e. radiative transfer) methods.
The other class of atmospheric compensation algorithms is based on empirical methods. These algorithms are typically purely empirical in that they require no prior knowledge of the atmosphere or environment, and no physical modeling of radiation transport processes is needed. Instead, they rely on the presence of surfaces of known reflectance within the scene to calibrate the sensor-atmosphere system. This usually implies finding a linear relationship that transforms the measured arbitrarily-calibrated measurement of these known targets into their known reflectance. It is then assumed that this linear relationship holds for all other pixels in the scene, giving rise to a reflectance image. Since there are two unknowns to be found—the gain and the offset—at least two known targets that are sufficiently different in all spectral bands must be present in the scene.
This type of approach is known as an Empirical Line Method (ELM). ELM is independent of any knowledge or pre-calibration of the sensor, since the effect of the sensor is implicitly included in the calibration target measured signatures. In addition, the ELM algorithm is relatively compact and simple and does not depend on external models. This translates to much faster calculations that are easily implemented into parallel (multi-processor) or super-parallel (GPU) architectures. Furthermore, in ELM the knowledge of atmospheric properties or aerosol load is not necessary since their effect is automatically considered in the calculation of the gain and offset parameters.
However, empirical methods have some drawbacks. These drawbacks may include but are not limited to the following. One drawback is that the assumption is made that the scene properties are uniform: the calibration in gain and offset is applied identically throughout the entire image. This excludes directly taking into account broken clouds, adjacency effects or spatial variability in aerosol load or water vapor content.
Another disadvantage may be that surfaces of known reflectances that are sufficiently different throughout their spectral signature must be present within the image in order to find the required gains and offsets at each spectral band. In other words, knowledge of image elements in the scene must be known a priori. Therefore empirical methods are often not well suited to applications where there is little or no prior information about a potential scene. This condition is rarely fulfilled in operational contexts, which may include certain surveillance and military operations.
In addition to the above mentioned class of atmospheric compensation methods that are based on empirical methods, a more recent family of algorithms has been introduced in an attempt to alleviate some of the drawbacks of purely empirical methods. This family of algorithms may be referred to as empirical-statistical algorithms. For example, the Quick Atmospheric Correction (QUAC™) attempts to eliminate the principal inconvenience of the traditional ELM algorithm in operational contexts, that is, to eliminate the need for a priori knowledge of scene element reflectances.
The QUAC family of methods is based on a number of statistical observations that seem to hold on a number of hyperspectral and multispectral images: notably, that the mean reflectance of many scene endmembers (at least 10) is constant (within a multiplicative constant) from scene to scene, as long as the scene is diversified enough. The QUAC method also relies on the presence of a dark (very low reflectance) pixel somewhere in the scene. These two hypotheses are then used to calculate the gain and offset parameters similarly to the ELM method. Vegetation pixels can be used to calculate the multiplicative factor that transforms reflectance from relative to absolute, since vegetation reflectance amplitude above the red edge is relatively predictable.
In a 2012 paper describing some revision to the QUAC algorithms, it is explained that although the above described observation on which QUAC is based holds for a wide variety of scenes, there are exceptions in which the observation does not hold. The identified exceptions include when there is vegetation, mud, or very shallow water in the spectral image. In this revision, the proposed solution is to detect the situations in which the performance of QUAC would degrade significantly (e.g. presence of mud, shallow water, etc.) and adjust the universal reflectance spectrum code accordingly.
Although the more recent revision of QUAC attempts to identify and account for the known situations in which QUAC would otherwise fail or suffer degraded performance, the reality is that there will likely always be cases where QUAC will fail since it is difficult if not impossible to predict all of these situations in advance. For example, it has been observed by the present inventors that the version of QUAC that is commercially available in ENVI™ 5.0 (a product or Exelis™) performs poorly or fails for some winter scenes. This is only one example of a type of scene for which a recent version of QUAC has poor performance. It is very likely that QUAC will have difficulties with other types of scenes as well. Therefore there is a need for a robust atmospheric compensation technique that does not rely as much on the identification and adjustment for problematic scenes or materials.
Therefore existing atmospheric compensation methods include first principles methods, purely empirical methods, and QUAC.
In addition, some atmospheric correction techniques use additional instrumentation to measure and collect information that can be used to compensate for atmospheric effects. For example, some hyperspectral sensors are used in conjunction with a fiber optic downwelling irradiance sensor (FODIS). The FODIS collects additional information that is used in the atmospheric compensation process.
Where the use or definition of a term or expression in a reference that is incorporated herein by reference is different or inconsistent with the use or definition of the term or expression as used herein, the use or definition of the term or expression provided herein applies.